// self stabilisation algorithm Beauquier, Gradinariu and Johnen
// gxn/dxp 18/07/02

// model is an mdp
nondeterministic

// module of process 1
module process1
	
	d1 : bool; // probabilistic variable
	p1 : bool; // deterministic variable
	
	[] (d1=d7) &  (p1=p7) -> 0.5 : (d1'=!d1) & (p1'=p1) + 0.5 : (d1'=!d1) & (p1'=!p1);
	[] (d1=d7) & !(p1=p7) -> (d1'=!d1);
	
endmodule

// add further processes through renaming
module process2 =process1[p1=p2 ,p7=p1, d1=d2 ,d7=d1] endmodule
module process3 =process1[p1=p3 ,p7=p2, d1=d3 ,d7=d2] endmodule
module process4 =process1[p1=p4 ,p7=p3, d1=d4 ,d7=d3] endmodule
module process5 =process1[p1=p5 ,p7=p4, d1=d5 ,d7=d4] endmodule
module process6 =process1[p1=p6 ,p7=p5, d1=d6 ,d7=d5] endmodule
module process7 =process1[p1=p7 ,p7=p6, d1=d7 ,d7=d6] endmodule

// cost in any state is 1 (expected number of steps)
rewards
	
	true : 1;
	
endrewards

// initial states - any state with more than 1 token, that is all states
init
	
	true
	
endinit